An Overview of Principal Component Analysis

Sasan Karamizadeh, Shahidan M. Abdullah, Azizah A. Manaf, Mazdak Zamani, Alireza Hooman

Advanced Informatics School(AIS), Universiti Teknologi Malayisa, Kuala Lumpur, Malaysia.
Faculty of Management (FOM), Multimedia University (MMU), Cyberjaya, Malaysia..

DOI: 10.4236/jsip.2013.43B031                

Abstract

The principal component analysis (PCA) is a kind of algorithms in biometrics. It is a statistics technical and used orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables. PCA also is a tool to reduce multidimensional data to lower dimensions while retaining most of the information. It covers standard deviation, covariance, and eigenvectors. This background knowledge is meant to make the PCA section very straightforward, but can be skipped if the concepts are already familiar.

Keywords

Biometric; PCA; Eigenvector; Covariance; Standard Deviation

Conflicts of Interest

The authors declare no conflicts of interest.

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